Connectivity in the cortex is organized at multiple scales 1-5, suggesting that scale-dependent correlated activity is particularly important for understanding the behavior of sensory cortices and their function in stimulus encoding. Here, we analyze the scale-dependent structure of cortical interactions by using maximum entropy models 6-9 to characterize multiple-tetrode recordings from primary visual cortex of anesthetized monkeys (Macaca mulatta). We compare the properties of firing patterns among local clusters of neurons (<300 microns) with neurons separated by larger distances (600-2500 microns). We find that local firing patterns are distinctive: while multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. While they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.

The existence of two classes of cells, simple and complex, discovered by Hubel and Wiesel in 1962, is one of the fundamental features of cat primary visual cortex. A quantitative measure used to distinguish simple and complex cells is the ratio between modulated and unmodulated components of spike responses to drifting gratings, an index that forms a bimodal distribution. We have found that the modulation ratio, when derived from the subthreshold membrane potential instead of from spike rate, is unimodally distributed, but highly skewed. The distribution of the modulation ratio as derived from spike rate can, in turn, be predicted quantitatively by the nonlinear properties of spike threshold applied to the skewed distribution of the subthreshold modulation ratio. Threshold also increases the spatial segregation of ON and OFF regions of the receptive field, a defining attribute of simple cells. The distinction between simple and complex cells is therefore enhanced by threshold, much like the selectivity for stimulus features such as orientation and direction. In this case, however, a continuous distribution in the spatial organization of synaptic inputs is transformed into two distinct classes of cells.Hubel and Wiesel first defined simple and complex cells of the primary visual cortex according to whether their receptive fields could be subdivided into separate ON and OFF subregions 1,2 . This classification scheme is still widely used and correlates well with a cell's laminar position and synaptic connectivity [3][4][5] . A more quantitative method of distinguishing the two cell classes relies on responses to drifting gratings 6 . The firing rates of simple cells are strongly modulated at the grating temporal frequency and therefore have a large fundamental Fourier component (R 1 ) relative to the mean elevation in firing (R 0 ); conversely, complex cells respond with an elevation in firing rate that is only weakly modulated, so they have a small R 1 relative to R 0 . The modulation ratio R 1 /R 0 , in a large population of cortical cells, forms a clearly bimodal distribution, the two groups corresponding well with Hubel and Wiesel's definition of simple and complex 6 . The ratio is therefore widely used to distinguish simple and complex cells, and its bimodal distribution provides strong evidence that the two classes are fundamentally distinct and receive distinct patterns of synaptic inputs 7 .Correspondence should be addressed to D.F. (ferster@northwestern.edu). Note: Supplementary information is available on the Nature Neuroscience website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. NIH Public Access Author ManuscriptNat Neurosci. Author manuscript; available in PMC 2010 August 4. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptThe rationale for using the R 1 /R 0 measure to identify simple and complex cells is intimately related to Hubel and Wiesel's hierarchical model of processing in visual cortex. In the model, simp...

In the primary visual cortex (V1), nearby neurons are tuned to similar stimulus features, and, depending on the manner and time scale over which neuronal signals are analyzed, the resulting redundancy may mitigate deleterious effects of response variability. We estimated information rates in the short-time scale responses of clusters of up to six simultaneously recorded nearby neurons in monkey V1. Responses were almost independent if we kept track of which neuron fired each spike but were redundant if we summed responses over the cluster. Redundancy was independent of cluster size. Summing neuronal responses to reduce variability discards potentially useful information, and the discarded information increases with cluster size.

In their pioneering studies of primary visual cortex, Hubel and Wiesel described the existence of two classes of cells, which they termed "simple" and "complex". The original classification scheme was based on a number of partly subjective tests of linear spatial summation. Later, investigators adopted an objective classification method based on the ratio between the amplitude of the first harmonic of the response and the mean spike rate (or the F(1)/F(0) ratio) when the neuron is stimulated with drifting sinusoidal gratings. This measure is bimodally distributed over the population and divides neurons into two classes that correspond closely to the classical definition by Hubel and Wiesel. Here we show that a simple rectification model can predict the observed bimodal distribution of F(1)/F(0) in primary visual cortex when the distributions of the intracellular response modulation and mean are unimodal. Thus, contrary to common belief, the bimodality of F(1)/F(0) does not necessarily imply the existence of two discrete cell classes. Furthermore, in reviewing the literature, we find no independent support for a simple/complex dichotomy. These results suggest that the existence of two distinct neural populations in primary visual cortex, and the associated hierarchical model of receptive field organization, need to be re-evaluated.

How do neurons in the primary visual cortex (V1) encode the contrast of a visual stimulus? In this paper, the information that V1 responses convey about the contrast of static visual stimuli is explicitly calculated. These responses often contain several easily distinguished temporal components, which will be called latency, transient, tonic, and off. Calculating the information about contrast conveyed in each component and in groups of components makes it possible to delineate aspects of the temporal structure that may be relevant for contrast encoding. The results indicate that as much or more contrast-related information is encoded into the temporal structure of spike train responses as into the firing rate and that the temporally coded information is manifested most strongly in the latency to response onset. Transient, tonic, and off responses contribute relatively little. The results also reveal that temporal coding is important for distinguishing subtle contrast differences, whereas firing rates are useful for gross discrimination. This suggests that the temporal structure of neurons' responses may extend the dynamic range for contrast encoding in the primate visual system.